Transcriptome analysis of heat stressed LMH cells and related gene enrichment tools
Date
2015
Authors
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Journal ISSN
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Publisher
University of Delaware
Abstract
Huge economic loss is caused by heat-related stress in poultry industries in the United States. Studying the molecular mechanism underlying heat stress is important for improving chicken egg and meat production. In the first study, we have used RNA-Seq to identify heat stress responsive genes in the male white-leghorn chicken hepatocellular (LMH) cell line. High throughput technologies are being used to simultaneously study the expression patterns of large numbers of genes. Typically these studies involve examining how genes are expressed in two or more biological states (e.g. heat stress vs. normal condition), with the goal of understanding how biological differences affect transcription. This can generate lists containing hundreds to thousands of differentially expressed genes that are challenging to interpret. One approach to understanding the underlying biology of large gene lists is to group the responsive genes to knowledge bases such as pathways. However, no popular pathway databases, such as KEGG and Reactome, have complete chicken pathway data. Reactome is a popular human centric metabolic and signaling pathway database that relies on orthology between genomic sequences to predict pathways in other species. This approach is valuable, but will miss genes identified in transcriptomes that have not yet been identified in the respective genomic sequence. My second study extends the Reactome approach to use orthology information based on transcriptome data to annotate chicken pathways and create a web-based chicken pathway analysis and visualization tool ( http://raven.anr.udel.edu/~sunliang/pathway/ ). Second approach to help the researcher interpret large gene datasets is to associate genes and informative terms (iTerm) that are obtained from the biomedical literature using the eGIFT text-mining system. However, examining large lists of iTerm and gene pairs is a daunting task. My third study is to develop WebGIVI, an interactive web-based visualization tool to explore gene:iTerm pairs ( http://raven.anr.udel.edu/~sunliang/webgivi/index.php ). The transcriptome analysis of heat stressed LMH cells help us further understand heat stress mechanism. The limitation of bioinformatics analysis tools in this study also encouraged us to create two bioinformatics enrichment tools, WebCHRIP and WebGIVI. These two tools can facilitate the enrichment of large gene lists, and help biologists to generate integrated biological hypotheses.